{"title":"Mathematical relationships between control group variability and assay quality metrics","authors":"Andrew Lim","doi":"10.1016/j.slasd.2023.02.003","DOIUrl":null,"url":null,"abstract":"<div><p>Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z’-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z’-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.</p></div>","PeriodicalId":21764,"journal":{"name":"SLAS Discovery","volume":"28 5","pages":"Pages 203-210"},"PeriodicalIF":2.7000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SLAS Discovery","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2472555223000163","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z’-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z’-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.
期刊介绍:
Advancing Life Sciences R&D: SLAS Discovery reports how scientists develop and utilize novel technologies and/or approaches to provide and characterize chemical and biological tools to understand and treat human disease.
SLAS Discovery is a peer-reviewed journal that publishes scientific reports that enable and improve target validation, evaluate current drug discovery technologies, provide novel research tools, and incorporate research approaches that enhance depth of knowledge and drug discovery success.
SLAS Discovery emphasizes scientific and technical advances in target identification/validation (including chemical probes, RNA silencing, gene editing technologies); biomarker discovery; assay development; virtual, medium- or high-throughput screening (biochemical and biological, biophysical, phenotypic, toxicological, ADME); lead generation/optimization; chemical biology; and informatics (data analysis, image analysis, statistics, bio- and chemo-informatics). Review articles on target biology, new paradigms in drug discovery and advances in drug discovery technologies.
SLAS Discovery is of particular interest to those involved in analytical chemistry, applied microbiology, automation, biochemistry, bioengineering, biomedical optics, biotechnology, bioinformatics, cell biology, DNA science and technology, genetics, information technology, medicinal chemistry, molecular biology, natural products chemistry, organic chemistry, pharmacology, spectroscopy, and toxicology.
SLAS Discovery is a member of the Committee on Publication Ethics (COPE) and was published previously (1996-2016) as the Journal of Biomolecular Screening (JBS).